---

license: gemma

base_model: lmstudio-community/gemma-4-26B-A4B-it-QAT-GGUF

pipeline_tag: text-generation

library_name: llama.cpp

language:

- en

tags:

- gguf

- llama.cpp

- gemma

- factbank

- security

- pentesting

- reverse-engineering

- retrieval

---

> ⚙️ Recommended runtime settings — gemma-native sampling temperature 1.0, top_k 64, top_p 0.95, min_p 0.01 (the min_p 0.01 floor prevents the reasoning-loop empty-answer issue), context length ≥ 16k (32k recommended), and a generous max_tokens when running with thinking on. The Gemma-4 thinking path needs --jinja.

gemma-4-26B-A4B-offsec-expert (GGUF)

*Base gemma-4-26B-A4B-it with an Offensive Security & Reverse Engineering FactBank baked into its chat-template.* The model answers

correctly about post-cutoff / breaking-change APIs in 17 offensive-security / RE libraries — not by fine-tuning,

but by carrying a searchable bank of 489 landmine facts that fires inside llama.cpp at inference

time. Weights are untouched (only the GGUF chat-template was rewritten); no external RAG service.

> The model supplies the reasoning; the bank supplies the knowledge it was never trained on.

> 🔗 Full project — all experts, methodology, per-question transcripts, and benchmarks:

> github.com/mhndayesh/experts-models

What this expert is about

Red-team tooling and binary analysis move fast and break APIs constantly — a v2→v3 rewrite (volatility3), a Rust rewrite (yara-x), a tool renamed outright (CrackMapExec → netexec). A stock model answers about these from stale training and is confidently wrong. This bank carries the exact breaking-change facts so the model gets them right.

Everything here is a landmine fact: post-cutoff, reverses-a-trained-habit, or a silent failure the model

wouldn't otherwise catch. Every fact is quote-grounded to a real migration guide / changelog line. Examples

the base gets wrong and this model gets right: the CrackMapExec→netexec (nxc) rename, the volatility3 v3 plugin shape (PluginInterface+TreeGrid+run), yara-x's Rust rule/API differences, capstone v6, frida 17.

Libraries in the bank (17) — 489 facts total

| library | facts | what it is / the churn |

|---|---:|---|

| angr | 103 | binary analysis / symbolic execution — API migration |

| ldap3 | 63 | LDAP client (AD tooling) — async module rename, constant renames |

| capstone | 58 | disassembly framework — the v6 API break |

| netexec | 52 | network execution toolkit — the CrackMapExec successor (nxc) |

| pwntools | 45 | CTF / exploit-dev toolkit — v5 changes |

| nuclei | 27 | template-based vulnerability scanner — v3 flags/schema |

| responder | 27 | LLMNR/NBT-NS/mDNS poisoner — option changes |

| impacket | 18 | network-protocol toolkit — example/API changes |

| volatility3 | 18 | memory forensics — the v2→v3 rewrite (PluginInterface/TreeGrid/run) |

| frida | 16 | dynamic instrumentation — the v17 split |

| unicorn | 15 | CPU emulator — the v2 API |

| plaso | 12 | timeline forensics — tool/flag changes |

| certipy | 10 | AD CS abuse — v5 command surface |

| yara-x | 10 | YARA rewritten in Rust — rule/API differences |

| dnfile | 6 | .NET PE parsing — API changes |

| bloodhound-py | 5 | BloodHound Python ingestor — CE changes |

| pefile | 4 | PE-file parsing — API changes |

Where the facts come from (mined sources)

Each library's facts were extracted from its migration guide / changelog (source targeting is the whole

game — a migration guide, not release-note noise), then quote-verified against the source line:

• angr migration guide
• ldap3 changelog
• capstone v6 changelog
• netexec migration notes
• pwntools 5 changelog
• nuclei 3 changelog
• responder changelog
• impacket changelog
• volatility3 migration guide
• frida 17 notes
• unicorn 2 changelog
• plaso changelog
• certipy v5 notes
• yara-x differences doc
• dnfile changelog
• bloodhound-py docs
• pefile changelog

Full provenance (the mined source docs themselves) lives in the repo under

v2/extractor/experts/offensive-security-re/sources/.

Results (this model — hand-verified)

Same 44 adversarial landmine questions, base vs. this baked model, identical prompts (the bank injects

in-engine). Config: thinking-on + authority framing, Gemma-native sampling.

| | base 26B-A4B | this model | Δ | error-closure* |

|---|---|---|---|---|

| /44 landmine questions | 17/44 (38.6%) | 43/44 (97.7%) | +26 | 96% |

\* of the answers the base got wrong, the fraction the bank fixed. Every answer was hand-scored — an

automated substring check miscounts in both directions (it fails a correct answer that names the old API as a

contrast, and passes a semantically wrong one).

How to run

The bank lives in the chat-template, so retrieval needs the template applied — run on llama.cpp:

llama-server -m gemma-4-26B-A4B-offsec-expert-Q4_0.gguf --jinja --port 8080 --ctx-size 8192

Then query normally — the same prompt you'd send the base model; the bank fires automatically for covered

topics. Sampling — use Gemma-native, not a bare low temperature:

temperature 1.0, top_k 64, top_p 0.95, min_p 0.01 (the min_p floor prevents reasoning-loop empty answers).

Best accuracy (authority + thinking) — this is how the numbers above were measured. Send

chat_template_kwargs={"enable_thinking": true} and a system prompt telling the model the looked-up facts

are verified and supersede its training. A reasoning model otherwise tends to "correct" an injected fact back

to its trained prior; authority framing holds the fact.

Limitations

• Scoped to the 17 covered libraries. Outside them it's the base model.
• Supplies knowledge, not reasoning. A multi-step transform can still fail even with the right fact retrieved.
• Retrieval gate is token-based, with aliases. This bake includes the gate-alias fix — a rename's OLD

name (e.g. "CrackMapExec") also opens the tab — but a wholly unrelated phrasing may still miss.

• Numbers are hand-scored landmine tests, not a general coding benchmark.

Papers

The write-ups behind this project (PDFs, rendered on GitHub): Research Report · Idea · Technical · Verdict · Evidence Ledger

Provenance & license

• Base: lmstudio-community/gemma-4-26B-A4B-it-QAT-GGUF (Q4_0). This model = that GGUF with tokenizer.chat_template rewritten to embed an

inverted-index retriever + the bank (factbank.version 0.4.0, gate-alias fix applied).

• License: Google Gemma Terms of Use (license: gemma) — a gemma-4 derivative. The fact bank is from

the FactBank project (see the repo LICENSE); mined sources keep their own licenses.

• Source & method: github.com/mhndayesh/experts-models.